Mathematical optimization techniques for resource allocation and spatial multiplexing in spectrum

223 Pages·2016·3.78 MB·English

Loughborough University
Institutional Repository
Mathematical optimization
techniques for resource
allocation and spatial
multiplexing in spectrum
sharing networks
ThisitemwassubmittedtoLoughboroughUniversity’sInstitutionalRepository
by the/an author.
Additional Information:
• ADoctoralThesis. Submittedinpartialful(cid:28)lmentoftherequirementsfor
the award of Doctor of Philosophy of Loughborough University.
Metadata Record: https://dspace.lboro.ac.uk/2134/11677
Publisher: (cid:13)c Jie Tang
Please cite the published version.
Mathematical Optimization Techniques
for Resource Allocation and Spatial
Multiplexing in Spectrum Sharing
Networks
by
Jie Tang
A Doctoral Thesis submitted in partial fulfilment of the requirements
for the award of the degree of Doctor of Philosophy (PhD), at
Loughborough University.
September 2012
Advanced Signal Processing Group,
School of Electronic, Electrical and Systems Engineering,
Loughborough University, Loughborough
Leicestershire, UK, LE11 3TU.
⃝c by Jie Tang, 2012
CERTIFICATE OF ORIGINALITY
This is to certify that I am responsible for the work submitted in this thesis,
that the original work is my own except as specified in acknowledgements
or in footnotes, and that neither the thesis nor the original work contained
therein has been submitted to this or any other institution for a degree.
.................................. (Signed)
Jie Tang (Candidate)
I dedicate this thesis to my dear parents Jianhua Tang and Jianhua Guo
and my beloved wife Jieqian Zhou.
Abstract
Due to introduction of smart phones with data intensive multimedia and
interactive applications and exponential growth of wireless devices, there is
a shortage for useful radio spectrum. Eventhough the spectrum has become
crowded, manyspectrumoccupancymeasurementsindicatethatmostofthe
allocatedspectrumisunderutilised. Henceradicallynewapproachesinterms
of allocation of wireless resources are required for better utilization of radio
spectrum.
This has motivated the concept of opportunistic spectrum sharing or
the so-called cognitive radio technology that has great potential to improve
spectrumutilization. Thecognitiveradiotechnologyallowsanopportunistic
user namely the secondary user to access the spectrum of the licensed user
(known as primary user) provided that the secondary transmission does not
harmfully affect the primary user. This is possible with the introduction
of advanced resource allocation techniques together with the use of wireless
relays and spatial diversity techniques.
In this thesis, various mathematical optimization techniques have been
developed for the efficient use of radio spectrum within the context of spec-
trum sharing networks. In particular, optimal power allocation techniques
and centralised and distributed beamforming techniques have been devel-
oped. Initially, an optimization technique for subcarrier and power alloca-
tion has been proposed for an Orthogonal Frequency Division Multiple Ac-
cess (OFDMA) based secondary wireless network in the presence of multiple
primary users. The solution is based on integer linear programming with
multiple interference leakage and transmission power constraints. In order
to enhance the spectrum efficiency further, the work has been extended to
allow multiple secondary users to occupy the same frequency band under a
multiple-input and multiple-output (MIMO) framework. A sum rate maxi-
mizationtechniquebasedonuplink-downlinkdualityanddirtypapercoding
has been developed for the MIMO based OFDMA network. The work has
also been extended to handle fading scenarios based on maximization of er-
godic capacity. The optimization techniques for MIMO network has been
extended to a spectrum sharing network with relays. This has the advan-
tage of extending the coverage of the secondary network and assisting the
primary network in return for the use of the primary spectrum. Finally, in-
stead of considering interference mitigation, the recently emerged concept of
interference alignment has been used for the resource allocation in spectrum
sharing networks. The performances of all these new algorithms have been
demonstrated using MATLAB based simulation studies.
Contents
1 INTRODUCTION 1
1.1 Evolution of Wireless Communication Systems 1
1.2 Motivation for Spectrum Sharing Cognitive Radio Networks 4
1.3 Thesis Outline 8
2 RESOURCEALLOCATIONANDSPATIALMULTIPLEX-
INGTECHNIQUESFORWIRELESSCOMMUNICATION
SYSTEM 12
2.1 Introduction 12
2.2 Multi-carrier Modulation Technology 14
2.2.1 Introduction to OFDM 15
2.2.2 Guard Interval 17
2.2.3 Mathematical Transformation 18
2.2.4 Overview of OFDMA 19
2.2.5 Related Works on Resource Allocation for OFDMA
Based Wireless Network 20
2.3 Beamforming Techniques 21
2.3.1 Receiver Beamforming Techniques 22
2.3.2 Transmitter Beamforming Techniques 23
2.4 Spatial Multiplexing Techniques 25
2.4.1 System Architecture for MIMO OFDM Based Spatial
Multiplexing 28
i
ii
Contents
2.5 Capacity of Wireless Networks 29
2.5.1 Capacity of SISO AWGN Channel 29
2.5.2 MIMO Channel Capacity 30
2.5.3 Duality of Gaussian Multiple-Access and Broadcast
Channels 31
2.6 Interference Alignment Techniques for Wireless Network 35
3 RESOURCE ALLOCATION TECHNIQUES FOR OFDMA
BASED SPECTRUM SHARING NETWORKS 40
3.1 Introduction 41
3.2 OptimalAdaptiveBitLoadingandSubcarrierAllocationTech-
niques for OFDMA Based Cognitive Radio Systems 42
3.2.1 System Model 43
3.2.2 Problem Statement 46
3.2.3 Integer Linear Programming Problem Formulation 48
3.2.4 Simulation Results 50
3.3 Suboptimal User Maximization Algorithm 53
3.3.1 Problem Formulation 53
3.3.2 Algorithms Using Integer Programming 55
3.3.3 Complexity Analysis 58
3.3.4 Simulation Results 59
3.4 Conclusion 61
4 SUM RATE MAXIMIZATION FOR SPECTRUM SHAR-
ING MULTIUSER MIMO NETWORKS 63
4.1 Introduction 64
4.2 SumRateMaximizationTechniqueforSpectrumSharingMIMO-
OFDM Broadcast Channels 66
4.2.1 System Model Combining MIMO-OFDM and Cogni-
tive Radio Network 66
iii
Contents
4.2.2 Problem Statement 67
4.2.3 Dual MAC Weighted Sum Rate Maximization Prob-
lem for CR-MIMO-OFDM-BC 70
4.2.4 The Optimization for the Solution of Qm in Problem
n,k
4.5 72
4.2.5 Mapping MAC Optimization Solution to BC Solution 74
4.2.6 The Complete Solution of the Original Problem 75
4.2.7 Simulation Results 76
4.3 SumRateMaximizationforSpectrumSharingMultiuserMIMO
Network under Rayleigh Fading 81
4.3.1 System Model and Problem Statement 81
4.3.2 Dual MAC Optimization Problem 83
4.3.3 Simulation Results 89
4.4 AnOptimalResourceAllocationTechniqueforSpectrumShar-
ing MIMO Wireless Relay Network 90
4.4.1 System Model 91
4.4.2 ResourceAllocationSchemeforSpectrumSharingMIMO
Based Wireless Relay Systems 93
4.4.3 Simulation Results 99
4.5 Conclusion 102
5 BEAMFORMINGANDTEMPORALPOWEROPTIMIZA-
TION FOR SPECTRUM SHARING NETWORKS 104
5.1 Introduction 105
5.2 System Model and Problem Statement 106
5.3 Iterative Algorithms using Convex Technique 111
5.4 Simulation Results 115
5.5 Conclusion 118
6 INTERFERENCEALIGNMENTTECHNIQUESFORSPEC-
iv
Contents
TRUM SHARING NETWORKS 120
6.1 Introduction 121
6.2 Interference Alignment Techniques for Multiple Input Multi-
ple Output Multi-Cell Interfering Broadcast Channels 122
6.2.1 System Model 123
6.2.2 Extension of the Grouping Method 127
6.2.3 The Proposed Interference Alignment Scheme using
BC-MAC Duality 135
6.2.4 Simulation Results 143
6.3 InterferenceCancelationandAlignmentTechniquesforMIMO
Cognitive Relay Networks 147
6.3.1 System Model 148
6.3.2 The Maximum Achievable Degrees of Freedom for In-
terference Cancelation Based MIMO Cognitive Relay
Networks 151
6.3.3 The Maximum Achievable Degrees of Freedom for In-
terference Alignment Based MIMO Cognitive Relay
Networks 158
6.3.4 Simulation Results 163
6.4 Conclusion 167
7 SUMMARY, CONCLUSION AND FUTURE WORK 169
7.1 Summary and Conclusions 169
7.2 Future Work 172
Statement of Originality
The contributions of this thesis are mainly on the development of various
resource allocation and spatial multiplexing techniques for spectrum sharing
networks. The following aspects of this thesis are believed to be originals:
• Resource allocation and admission control techniques were proposed
for an Orthogonal Frequency Division Multiple Access (OFDMA)-
based cognitive radio network in Chapter 3. In particular, an inte-
ger linear programming based optimization technique was proposed
to optimally allocate subcarriers, power and bits to secondary users
while satisfying the data rate and bit error rate (BER) requirements
foreachsecondaryuser[5]. Furthermore,tosolveaproblemwherethe
number of users seeking access to the network exceeds the available
resources, a suboptimal optimization algorithm based on integer pro-
gramming was proposed to admit as many secondary users as possible
while allocating subcarriers and bits to each admitted user in such
a way that the interference leakage to the primary users is below a
specific threshold [7].
• A weighted sum rate maximization and rate balancing techniques
have been proposed for Multiple-Input and Multiple-Output Orthogo-
nal Frequency Division Multiplexing (MIMO-OFDM) based spectrum
sharing broadcast channels [1]. This problem has been solved by con-
verting the MIMO-OFDM channel into block diagonal form and us-
ing the principle of broadcast channel - multiple access channel (BC-
MAC) duality. In addition, the sum rate maximization problem was
also solved for spectrum sharing MIMO BC under Rayleigh fading
environment using multiple auxiliary variables, Karush-Kuhn-Tucker
(KKT) optimality conditions and BC-MAC duality [11]. The work
has also been extended to consider rate balancing in spectrum sharing